Mapping cracks on port concrete pavements by analyzing structural health monitoring metadata with computer vision-based techniques

Ports act as hubs for international trade and transport, strengthening the economies of the regions they serve. To maintain their undisrupted operations, efficient port management systems rely on Structural Health Monitoring practices to detect defects and assess infrastructure performance. Regarding port concrete pavements, condition assessment includes crack detection. Currently, advanced algorithms and methodologies for image processing or machine learning applications are used for surface crack detection with images captured during in-situ inspections. The growing urge to employ unmanned aerial vehicles (UAVs) equipped with high-resolution cameras is driving further research into image processing methods. This study provides an insightful approach for real-time crack detection in port concrete pavements that takes advantage of the geospatial information included in UAV imagery. The proposed methodology is based on the synergetic application of programming and Geographic Information System tools. Widely used crack detection methods and algorithms are herein enhanced with geospatial analysis modules that help to manage photogrammetry metadata generated by processing UAV data. Geographic Information System tools are employed to add a new perspective to crack detection by supporting the visualization and interpretation of geospatial processed images to locate cracks and examine crack propagation. The investigation includes a periodic field test conducted at Lavrio Port, a Greek port located at the southeastern tip of Attica. Overall, the combined methodology returns results with high accuracy (approximately 95%), thus having a practical application in the engineering community that shifts to scalable solutions for mapping cracks in large port concrete surfaces remotely inspected with UAVs.